There is a growing need for automatic verification and validation of banknotes of different currencies and denominations in a simple, reliable, and cost effective manner. This is required, for example, in self-service apparatus which receives banknotes, such as self-service kiosks, ticket vending machines, automated teller machines arranged to take deposits, self-service currency exchange machines and the like. Automatic verification of other types of valuable media such as passports, checks and the like is also required.
Previously, manual methods of media validation have involved image examination, transmission effects such as watermarks and thread registration marks, feel and even smell of banknotes, passports, checks and the like. Other known methods have relied on semi-overt features requiring semi-manual interrogation. For example, using magnetic means, ultraviolet sensors, fluorescence, infrared detectors, capacitance, metal strips, image patterns and similar. However, by their very nature these methods are manual or semi-manual and are not suitable for many applications where manual intervention is unavailable for long periods of time. For example, in self-service apparatus.
There are significant problems to be overcome in order to create an automatic media validator. For example, many different types of currency exist with different security features and even substrate types. Within those different denominations also exist commonly with different levels of security features. There is therefore a need to provide a generic method of easily and simply performing currency validation for those different currencies and denominations.
Put simply, the task of a currency validator is to determine whether a given banknote is genuine or counterfeit. Previous automatic validation methods typically require a relatively large number of examples of counterfeit banknotes to be known in order to train the classifier. In addition, those previous classifiers are trained to detect known counterfeits only. This is problematic because often little or no information is available about possible counterfeits. For example, this is particularly problematic for newly introduced denominations or newly introduced currency.
In an earlier paper entitled, “Employing optimized combinations of one-class classifiers for automated currency validation”, published in Pattern Recognition 37, (2004) pages 1085-1096, by Chao He, Mark Girolami and Gary Ross (two of whom are inventors of the present application) an automated currency validation method is described (Patent No. EP1484719, US2004247169). This involves segmenting an image of a whole banknote into regions using a grid structure. Individual “one-class” classifiers are built for each region and a small subset of the region specific classifiers are combined to provide an overall decision. (The term, “one-class” is explained in more detail below.) The segmentation and combination of region specific classifiers to achieve good performance is achieved by employing a genetic algorithm. This method requires a small number of counterfeit samples at the genetic algorithm stage and as such is not suitable when counterfeit data is unavailable.
There is also a need to perform automatic currency validation in a computationally inexpensive manner which can be performed in real time.
Another problem relates to situations in which automatic currency validation systems are in place and are relatively successfully operating in a given environment. For example, that environment comprises a population of genuine and counterfeit banknotes with a given quality range and distribution. If sudden changes to that environment occur it is typically difficult for such automated currency validation systems to adapt. For example, suppose the new higher quality counterfeit banknotes suddenly begin to enter the banknote population. Police intelligence, manual validation and other information sources might indicate the presence of the higher quality counterfeit banknotes. In this situation, if a bank or other provider finds counterfeit notes are being accepted at automated currency validation machines, a commercial decision is typically made to stop using those machines. However, this is costly because manual validation needs to be made instead and customers are inconvenienced. Significant time and cost also needs to be invested to upgrade the automated currency validation systems to cope with the higher quality counterfeit banknotes.
Many of the issues mentioned above also apply to validation of other types of valuable media such as passports, checks and the like